Vinyl as Fine Wine: The Role of Expectation on the Perception of Music Format
Bibliographic record
Abstract
While vinyl, compact discs, and even eight-track tapes were traditionally promoted to consumers as producing superior sound, the introduction of compressed digital music, such as mp3s, was markedly different. Initially, one of the primary selling features of digital music was convenience and portability rather than sound quality. Recently, vinyl music sales have experienced a substantial resurgence. Waveforms from vinyl represent recorded music more accurately than compressed digital formats and have the potential to produce better sound. Even so, most music listeners do not reliably listen to music on audiophile quality high-end equipment. For this reason, we believe one aspect of vinyl sales is the expectation that vinyl quality is superior. In this study, we sought to isolate the contribution of expectation to perceived sound quality. Participants were asked to listen to a selection of music on either vinyl or mp3. Some participants were told that they were listening to vinyl when the musical selection was an mp3, while others were told they were listening to an mp3 while actually listening to vinyl. A multivariate analysis through a Canonical Correlation Analysis established that expectation of music format quality drove post-listening evaluations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".